https://www.kaggle.com/new-york-state/nys-children-in-foster-care-annually https://www.ncsc.org/Microsites/EveryKid/Home/Data-and-Reform-Efforts/Data-By-State.aspx https://www.acf.hhs.gov/cb/resource/trends-in-foster-care-and-adoption
library(readxl)
library(tidyverse)
library(viridis)
library(plotly)
library(sf)
library(leaflet)
#national dataset
nation_data<-read_excel("data/national_afcars_trends_2009_through_2018.xlsx",sheet="Data")
#State dataset
#Numbers of Children Served in Foster Care, by State
state_served <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Served!A8:K60") %>%
gather(year,Served,'FY 2009':'FY 2018')
#Numbers of Children in Foster Care on September 30th, by State
state_inCare <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="In Care on September 30th!A8:K60") %>%
gather(year,InCare_Sep30,'FY 2009':'FY 2018')
#Numbers of Children Entering Foster Care, by State
state_entered <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Entered!A8:K60") %>%
gather(year,Entered,'FY 2009':'FY 2018')
#Numbers of Children Exiting Foster Care, by State
state_exited <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Exited!A8:K60") %>%
gather(year,Exited,'FY 2009':'FY 2018')
#Numbers of Children Waiting for Adoption, by State
state_waitingAdoption <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Waiting for Adoption!A8:K60") %>%
gather(year,Waiting_Adoption,'FY 2009':'FY 2018')
#Numbers of Children Waiting for Adoption Whose Parental Rights Have Been Terminated, by State
state_parentalRightsTerminated <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Parental Rights Terminated!A8:K60") %>%
gather(year,parental_rights_terminated,'FY 2009':'FY 2018')
#Numbers of Children Adopted, by State
state_adopted <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Adopted!A8:K60") %>%
gather(year,adopted,'FY 2009':'FY 2018')
merge_cols<-c("State","year")
#The merge argument only takes two values as input, so you have to do them separately:
#state_df<- merge(state_served,state_inCare,state_entered,state_exited,state_waitingAdoption,state_parentalRightsTerminated,state_adopted,by=c("State","year"))
state_data<- merge(state_served,state_inCare,by=merge_cols)
state_data<- merge(state_data,state_entered,by=merge_cols)
state_data<- merge(state_data,state_exited,by=merge_cols)
state_data<- merge(state_data,state_waitingAdoption,by=merge_cols)
state_data<- merge(state_data,state_parentalRightsTerminated,by=merge_cols)
state_data<- merge(state_data,state_adopted,by=merge_cols)
head(state_data)
## State year Served InCare_Sep30 Entered Exited Waiting_Adoption
## 1 Alabama FY 2009 9677 6179 3080 3498 1475
## 2 Alabama FY 2010 8119 5350 3063 2770 1271
## 3 Alabama FY 2011 8395 5253 3257 3143 1297
## 4 Alabama FY 2012 7907 4561 2763 3346 1156
## 5 Alabama FY 2013 7322 4435 3041 2888 1084
## 6 Alabama FY 2014 7520 4526 3192 2994 1044
## parental_rights_terminated adopted
## 1 882 638
## 2 757 606
## 3 701 447
## 4 543 587
## 5 615 532
## 6 573 544
us_states <- st_read("./shp/states.shp")
## Reading layer `states' from data source `/Volumes/GoogleDrive/My Drive/RWorkspace/VisualAnalytics-FinalProject/shp/states.shp' using driver `ESRI Shapefile'
## Simple feature collection with 51 features and 5 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -178.2176 ymin: 18.92179 xmax: -66.96927 ymax: 71.40624
## epsg (SRID): 4269
## proj4string: +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs
as_tibble(us_states)
## # A tibble: 51 x 6
## STATE_NAME DRAWSEQ STATE_FIPS SUB_REGION STATE_ABBR geometry
## <fct> <int> <fct> <fct> <fct> <MULTIPOLYGON [°]>
## 1 Hawaii 1 15 Pacific HI (((-160.0738 22.00418, -…
## 2 Washington 2 53 Pacific WA (((-122.402 48.22522, -1…
## 3 Montana 3 30 Mountain MT (((-111.4754 44.70216, -…
## 4 Maine 4 23 New Engla… ME (((-69.77728 44.07415, -…
## 5 North Dak… 5 38 West Nort… ND (((-98.73044 45.93827, -…
## 6 South Dak… 6 46 West Nort… SD (((-102.7884 42.9953, -1…
## 7 Wyoming 7 56 Mountain WY (((-104.0536 41.69822, -…
## 8 Wisconsin 8 55 East Nort… WI (((-87.74856 44.96162, -…
## 9 Idaho 9 16 Mountain ID (((-117.0263 43.67903, -…
## 10 Vermont 10 50 New Engla… VT (((-73.25806 42.74606, -…
## # … with 41 more rows
state_data_2009 <- state_data %>% filter(year == 'FY 2009') %>% rename(STATE_NAME = State)
us_states_mapped <- inner_join(us_states,state_data_2009,by="STATE_NAME")
## Warning: Column `STATE_NAME` joining factor and character vector, coercing into
## character vector
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
head(df)
## code state category total.exports beef pork poultry dairy fruits.fresh
## 1 AL Alabama state 1390.63 34.4 10.6 481.0 4.06 8.0
## 2 AK Alaska state 13.31 0.2 0.1 0.0 0.19 0.0
## 3 AZ Arizona state 1463.17 71.3 17.9 0.0 105.48 19.3
## 4 AR Arkansas state 3586.02 53.2 29.4 562.9 3.53 2.2
## 5 CA California state 16472.88 228.7 11.1 225.4 929.95 2791.8
## 6 CO Colorado state 1851.33 261.4 66.0 14.0 71.94 5.7
## fruits.proc total.fruits veggies.fresh veggies.proc total.veggies corn wheat
## 1 17.1 25.11 5.5 8.9 14.33 34.9 70.0
## 2 0.0 0.00 0.6 1.0 1.56 0.0 0.0
## 3 41.0 60.27 147.5 239.4 386.91 7.3 48.7
## 4 4.7 6.88 4.4 7.1 11.45 69.5 114.5
## 5 5944.6 8736.40 803.2 1303.5 2106.79 34.6 249.3
## 6 12.2 17.99 45.1 73.2 118.27 183.2 400.5
## cotton
## 1 317.61
## 2 0.00
## 3 423.95
## 4 665.44
## 5 1064.95
## 6 0.00
Plotly Viridis color pallete - https://www.r-bloggers.com/how-to-use-viridis-colors-with-plotly-and-leaflet/
#Set hover text
us_states_mapped$hover <- with(us_states_mapped,paste(STATE_NAME,'<br>',"Served: ", Served))
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
plot_geo(us_states_mapped, locationmode = 'USA-states') %>%
add_trace(
z = ~Served, text = ~hover, locations = ~STATE_ABBR,
color = ~Served, colors = viridis_pal(option = "D")(3)
) %>%
colorbar(title = "Served") %>%
layout(
title = 'Orphans Served by each state in 2009<br>(Hover for breakdown)',
geo = g
)
We provide viridis colors to plotly using viridis_pal and by setting option argument to “D” the default “viridis” is selected. Other options are “A” for “magma” theme, “B” – “inferno” and “C” – “plasma”. Play with letters to check which one you like the most or which suits your plot the best.
https://stackoverflow.com/questions/43434898/choropleth-maps-in-r-using-leaflet-package
popup1 <- paste0("<span style='color: #7f0000'><strong>US State Values</strong></span>",
"<br><span style='color: salmon;'><strong>State: </strong></span>",
us_states_mapped$STATE_NAME,
"<br><span style='color: salmon;'><strong>Served: </strong></span>",
us_states_mapped$Served)
pal <- leaflet::colorFactor(viridis_pal(option = "D")(3), domain = us_states_mapped$Served)
leaflet(us_states_mapped) %>%
addProviderTiles("OpenStreetMap.Mapnik") %>%
addPolygons(data = us_states_mapped,
fillColor = ~pal(Served),
fillOpacity = 0.6,
color = "darkgrey",
weight = 1.5,
popup = popup1)
## Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs).
## Need '+proj=longlat +datum=WGS84'
# addLegend(pal = pal, values = ~Served, opacity = 0.7, title = NULL,
# position = "bottomright")